DNP875 Module 5 Sample Paper

Uncontrolled Type 2 Diabetes in a Rural Federally Qualified Health Center Panel: A Population Analysis and Person-Centered Intervention Plan

[Author Name]

Doctor of Nursing Practice Program, Aspen University

DNP875 Population Health and Person-Centered Care

Module 5 Assignment

[Faculty Name]

August 11, 2026

Original model document. The health center, its panel and the figures reported here are composites built for teaching; no real clinic, community or patient is described.

What this page is doingThe title names the condition, the population, the setting type and both halves of the paper, so a reader can tell before the first line that an analysis and a plan are coming and that the plan answers the analysis. Course and module lines carry the classroom's own vocabulary rather than an invented deliverable name. The composite line matters here more than in most papers, because a named health center would imply access to panel-level data that a model document does not hold.

Defining the Population and Its Measured Burden

River Bend Health is a composite federally qualified health center serving a three-county rural catchment of 61,000 residents from two sites. The population under analysis is defined narrowly enough to be counted: adults aged 18 and older with a coded diagnosis of type 2 diabetes and at least one visit to either site during the 12 months ending December 31, 2025. That definition produces a denominator of 1,840 patients. Of those 1,840, a total of 629 had a most recent glycated hemoglobin value above 9 percent, giving a poor-control rate of 34.2 percent for the 12-month window. A further 214 patients, or 11.6 percent of the denominator, had no A1C drawn at all in that period and are counted separately rather than folded into either group.

Stratifying that denominator is where the analysis begins to be useful. Poor control reached 41.7 percent among the 612 patients living more than 25 miles from either site, against 28.6 percent among those within 25 miles. It reached 44.1 percent among the 388 patients whose records show no insurance coverage, and 39.8 percent among the 421 patients with a documented preferred language other than English, a group served by two interpreter phone lines and no bilingual clinician. Patients aged 35 to 54 fared worse than those over 65, at 38.9 percent against 27.4 percent, which runs against the usual age gradient and points at working hours rather than at biology.

The consequences are already visible in use of care. Across the same 12 months the panel generated 96 emergency department visits with a primary diagnosis of hyperglycemia or diabetic ketoacidosis, 71 of them from patients in the poor-control group, and 14 lower-extremity wound admissions. Only 22.3 percent of the 1,840 had a documented diabetic eye examination in the period and 48.9 percent had a documented foot examination. These are not separate problems. They are what a population looks like when routine contact with the clinic breaks down, and each figure carries the same denominator so the paper can compare them without further explanation.

What this page is doingThe denominator is fixed by an explicit inclusion rule before any rate appears, so 34.2 percent means 629 of 1,840 adults over one named 12-month window and cannot drift later. Counting the 214 patients with no value drawn as their own group, rather than assuming they are controlled, is the honesty move that separates population work from reporting. Stratification then earns its space by producing a finding that argues with expectation, and the use-of-care figures reuse the same denominator so nothing needs re-explaining.

Determinants and the Failing Link in the Care Pathway

Behind the strata sit determinants the clinic does not control but must plan around. The catchment has no public transportation and one pharmacy for every 12,000 residents, with two of the three counties holding a single pharmacy each. Estimated household broadband access is 58 percent, which limits any remedy that assumes video visits. Twenty-one percent of adults in the catchment live below the federal poverty level and the two largest employers are a poultry processing plant and a school district, both with shift patterns that conflict with a clinic day ending at 5 p.m. A structured food access survey completed by 240 patients in the panel returned 43 percent reporting that food ran out before money did at least monthly.

Naming determinants is not the same as finding the failure, so the care pathway was traced step by step for the poor-control group. Of the 629 patients, 604 had at least one clinic visit in the period, so entry into care is not the failure. A treatment intensification was ordered for 511 of them, so clinical decision-making is not the failure either. Prescriptions reached a pharmacy for 487. First fills were completed by 402. Second fills within 90 days were completed by 231. Only 118 of the 629 returned for a follow-up A1C within 120 days of the intensification, which is 18.8 percent. The pathway holds until the patient leaves the room and then loses roughly two out of three people between the first fill and the follow-up value.

That pattern rules out the interventions most often proposed by reflex. A campaign to raise awareness addresses entry, which is intact. A clinician education module addresses prescribing, which is also intact. What fails is continuity after the visit: refills that require a second trip to a pharmacy 30 miles away, a follow-up appointment offered during a shift the patient cannot leave, and no contact between the visit and the next one. Any intervention that does not act on the interval between visits will move a process measure and leave the 34.2 percent where it stands, which is the reasoning that determines everything proposed in the next section.

What this page is doingThis is the paragraph pair that lifts the paper to doctoral level. Determinants are named with local figures rather than recited as a category, and then the pathway is traced with a count at every step, so the failure is located rather than asserted. Reporting the steps that work is what makes the finding credible. Ruling out awareness campaigns and clinician education by pointing at intact steps shows a writer reasoning from data toward an intervention instead of choosing one first and justifying it afterward.

Person-Centered Intervention Plan and Evaluation

The plan acts on the interval and nothing else. Three components carry it. Community health workers based in the two outlying counties hold a caseload of 90 patients each and make contact every 14 days by telephone, with a home visit where broadband and distance rule out anything else. Ninety-day prescriptions with mail delivery replace 30-day fills for every stable patient, removing the second and third pharmacy trip that the pathway data shows people do not make. Extended clinic hours until 7 p.m. on two evenings and one Saturday morning each month give shift workers a follow-up option that does not cost a day of pay. A pharmacist-led review by telephone is offered to every patient after an intensification.

Person-centered does not mean the plan was written for people rather than with them. The three components were selected from a longer list by a panel advisory group of nine patients drawn from the strata that fared worst, who ranked mail delivery and evening hours above a mobile clinic and above a text-message reminder service that the clinic had assumed would rank first. Their reasoning was practical and it changed the design: a reminder to attend an appointment that cannot be attended is not help. Goal setting at each contact follows the patient's stated priority, which in the group discussions was more often staying at work than reaching a number, and the A1C target is framed as what makes that possible.

Evaluation reuses the definitions above so the comparison is honest. The primary outcome is the percentage of the 1,840-patient denominator with a most recent A1C above 9 percent, recalculated at 12 months with the same inclusion rule, against the 34.2 percent baseline, with a target of 27 percent or lower. Process measures are the second-fill rate within 90 days, the follow-up A1C rate within 120 days of an intensification, and the contact rate for the community health worker caseloads. A balancing measure tracks visit no-show rates at the two sites so that extended hours are not achieved by thinning daytime capacity. Results are stratified by distance, coverage and language, because an average that hides the gap it was meant to close has answered nothing.

What this page is doingEach component answers a specific loss shown in the pathway data, so the plan reads as a consequence rather than as a menu. The advisory group paragraph is where person-centered stops being a claim: patients ranked the options, they overturned the clinic's assumption, and the design changed on the record. Evaluation then reuses the original denominator and inclusion rule, adds a balancing measure so gains are not borrowed from daytime capacity, and stratifies results by the axes that showed the gap.

References

Agency for Healthcare Research and Quality. (2023). 2023 national healthcare quality and disparities report. U.S. Department of Health and Human Services. https://www.ahrq.gov/research/findings/nhqrdr/

American Diabetes Association Professional Practice Committee. (2025). Standards of care in diabetes 2025. Diabetes Care, 48(Suppl. 1), S1-S352.

Centers for Disease Control and Prevention. (2024). National diabetes statistics report. U.S. Department of Health and Human Services. https://www.cdc.gov/diabetes/php/data-research/

Health Resources and Services Administration. (2024). Health center program uniform data system (UDS) resources. U.S. Department of Health and Human Services. https://bphc.hrsa.gov/data-reporting

Office of Disease Prevention and Health Promotion. (2024). Healthy People 2030: Social determinants of health. U.S. Department of Health and Human Services. https://health.gov/healthypeople

World Health Organization. (2021). Social determinants of health: Operational framework for monitoring social determinants of health equity. https://www.who.int

How this DNP 875 Module 5 example is structured

In many sections this module asks for an analysis of a defined population followed by an intervention plan built around what the people in it actually face; your classroom's instructions and rubric decide the exact form, so read the module's assignment page before you use this DNP875 Module 5 example as a shape. The order is diagnostic. The population is defined first, because a rate means nothing until the denominator behind it is fixed. The determinants come second, since a rate explains what is happening and not why. The care pathway is then traced step by step to find the link that fails, and only after that does the paper propose anything. The final section states how success would be measured and how the people in the population helped design it.

DNP875 Module 5 questions, answered

What does DNP875 Module 5 usually ask for?

In many sections this module asks for an analysis of a defined population with an intervention plan attached, showing how care is made person-centered for the people in it. The exact form belongs to your classroom: read the module's assignment page and rubric, since some sections want a community profile, others a full improvement plan, and the required headings differ.

How do I write a population rate so it holds up at doctoral level?

Carry the denominator and the window with the number every time. A poor-control rate of 34.2 percent means little alone; 629 of 1,840 adults meeting a stated inclusion rule over one named 12-month period can be checked, compared and recalculated later. Say how people with missing data were counted, since silence there is read as a rate you cannot defend.

What makes an intervention person-centered rather than just population-level?

Evidence that the people in the population shaped it. Show who was consulted, what they ranked highest, and what changed in the design as a result, including anything they rejected. Then show goals set from the patient's own stated priority rather than from a clinical target alone. A plan written for a population without its input is population-level, and graders can tell the difference.

Write yours, or have the desk draft it

This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.

DNP875 Module 5: sample paper, in real form